Search Engineer (Elasticsearch / OpenSearch / Solr)
Snapmint · Bengaluru
- Experience5–6 yrs
- SalaryNot disclosed
- Work modeonsite
- Levelsenior
- Posted14 Sept 2026
About Snapmint
Snapmint is hiring in Bengaluru in financial services. This role looks for around 5+ years of experience.
Skills
- Elasticsearch
- OpenSearch
- Apache Solr
- Information Retrieval
- BM25
- inverted indexes
- analyzers
- tokenization
- faceting
- query parsing
- relevance tuning
- spell correction
- synonym expansion
- query normalization
- transliteration
- attribute extraction
- semantic search
- embeddings
- Learning-to-Rank
- distributed systems
- caching strategies
- low-latency system design
- Java
- Node.js
- Rust
- Golang
The role
A search engineer at a consumer finance platform builds ecommerce discovery services using Information Retrieval, Elasticsearch, and Learning-to-Rank, and develops multilingual query understanding and scalable search APIs. The role also applies semantic search and distributed systems to improve relevance and low-latency retrieval.
Full job description
About the CompanySnapmint is on a mission of democratizing no/low-cost instalment purchases for next 200 Mn Indians. Of the 300 million credit eligible consumers in India, less than 30 Mn actively use credit cards. Snapmint is reinventing credit for the next 200 Mn consumers by providing them freedom to buy what they want and pay for them in installments without a credit card. In a short period of time Snapmint has reached over a million consumers in 2200 cities and has powered over 2000 crores worth of purchases. Snapmint was started by a closely-knit team of passionate bankers and technocrats from IIT Bombay.
About the RoleWe are looking for a Search Engineer who is passionate about solving complex search problems at scale from understanding natural, multilingual, and conversational user queries to delivering highly relevant, personalized search results across Snapmint's ecommerce ecosystem.
ResponsibilitiesDesign and build scalable, low-latency Search & Discovery services capable of serving millions of search requests.Develop intelligent query understanding capabilities including spell correction, synonym expansion, transliteration, multilingual query processing, intent detection, query rewriting, and attribute extraction.Build and Optimize indexing pipelines to continuously synchronize search indexes with catalog, pricing, inventory, and merchant updates.Design and improve search relevance using lexical search (BM25), semantic search, Learning-to-Rank, business signals, and personalization.Build search capabilities including autocomplete, query suggestions, facets, filters, zero-result recovery, and related search recommendations.Partner with Catalog, Personalization, Data Science, and Product teams to improve product discoverability and search quality.Define, monitor, and improve search quality metrics such as CTR, Conversion Rate, Zero-Result Rate, Recall, and Search Latency through experimentation and A/B testing.Drive architecture, performance optimization, observability, and production excellence for the Search platform.
Qualifications5+ years of experience designing and building scalable backend systems using modern programming languages (Java / Node.js / Rust / Golang etc)
Required SkillsStrong hands-on experience with Elasticsearch, OpenSearch, or Apache Solr.Deep understanding of Information Retrieval concepts including inverted indexes, analyzers, tokenization, BM25, faceting, query parsing, indexing, and relevance tuning.Experience building query understanding systems, including multilingual or transliterated search, spell correction, synonym expansion, and query normalization.Experience designing scalable search APIs.Experience building indexing pipelines for large-scale catalogs with near real-time updates.Experience implementing query understanding capabilities such as spell correction, synonym expansion, query normalization, transliteration, and attribute extraction.Good understanding of semantic search, embeddings and hybrid lexical-semantic retrieval.Experience with Learning-to-Rank (LTR) techniques or integrating ML-based ranking models into production search systems.Strong understanding of distributed systems, search performance optimization, caching strategies, and low-latency system design.
Preferred SkillsExperience building ecommerce or marketplace search.Experience using LLMs for query understanding, query rewriting, or conversational search.Experience running online experiments and A/B testing to improve search relevance.